My two previous articles on personalized customer experiences focused on the need to embrace AI<\/a> and the goal of AI that truly understands customer data<\/a>. I\u2019m going to continue to explore the framework I introduced in those articles here, moving to the lower-right quadrant: Agency.<\/p>\n I\u2019m not talking about AI\u2019s impact on marketing and advertising agencies<\/a>, though that\u2019s a topic getting attention in many circles. I originally considered labeling this quadrant as \u201cAgents\u201d\u00a0 \u2014 as an early indicator that AI-infused agents rose to a level<\/a> that deserved further discussion.\u00a0\u00a0<\/p>\n In the end, I chose \u201cAgency\u201d because I continue to believe we all have to personally embrace these martech trends in order to drive better customer experiences. In other words, we can claim agency (control) to use these technologies to benefit customers AND our business processes.\u00a0<\/p>\n Let\u2019s dive into how we claim agency over AI agents.<\/p>\n Many early agents were legacy products re-launched under an AI banner, with almost everything in tech labeled as \u201cAI\u201d in 2023.<\/a> Many of their capabilities were still rules based and relied on users to know how to configure them. In fact, the re-packaging of prior capabilities actually led to AI being over-hyped, creating uncertainty over how these new capabilities were different from established automation.<\/p>\n The primary difference between legacy agents and AI-powered agents is the latter\u2019s ability to be used and developed by less technical individuals through natural language. This is a continuation of the no-code trend<\/a> that lowers barriers to adoption. When you combine natural language with the ability to process data \u2014 both structured and unstructured \u2014 the capabilities start to open up even further.<\/p>\n The barriers to entry for better customer-driven marketing experiences will continue to drop, and we\u2019ll see a spectrum of capabilities that marketing leaders can leverage:<\/p>\n Let\u2019s break down a simple, hands-on example to address one of the most common limitations preventing personalization in customer-focused marketing: The ability to bring together disparate sources of data through a \u201clook-up\u201d use case.<\/p>\n The problem statement: A marketing team is limited by its inability to cross-match customer contact profiles (typically user roles and personas in the CRM<\/a>) with the specific product modules licensed by the account (stored in the companies ERP).<\/p>\n While we often hear predictions that AI will infuse various capabilities<\/a> for marketers, the direct embedding of AI in tools such as Microsoft Office apps like Excel via Copilot (or Google AI in Google Sheets) will further lower barriers<\/a> to various data management challenges.<\/p>\n Even if a marketing team doesn\u2019t use AI to \u201clearn Vlookup,\u201d the same outcomes can be achieved through natural language prompting.<\/p>\n To demonstrate this, I constructed sample data files (using ChatGPT) that would represent a typical set of customer data.<\/p>\n The first file had CRM<\/a> contacts in the following format:<\/p>\n Sample data set generated from ChatGPT.<\/em><\/p>\n I then constructed a different sample file that listed the product modules licensed by each account.<\/p>\n Sample account data.<\/em><\/p>\n The following are direct excerpts and results from my conversational prompts and testing with Google Gemini Advanced in September 2024.<\/p>\n I loaded the first file as background context and asked Gemini to complete analysis, while taking on the role of a senior analytics expert.<\/p>\n Without further prompting, Gemini provided multiple visualizations and a qualitative\/quantitative analysis of key customer attributes. Because I was focused on the product module use case, I uploaded the second sample file and used a simple prompt as follow-up.<\/p>\n If you\u2019ve run similar tests, you know Google\u2019s Gemini model often provides very detailed steps that it goes through to complete a task. In my test, it provided a detailed breakdown of Pandas Dataframes (a two-dimensional data structure that\u2019s part of Python\u2019s code library approach for analysis). I did not excerpt those screenshots below, and I found it notable that this may deter less-technical martech users. <\/p>\n Regardless, after a few more seconds it then came back with the following results and completed the task.<\/p>\n Responses generated from Google Gemini.<\/em><\/p>\n At the end of the analysis, it also provided a direct \u201cExport to Sheets\u201d link that allowed me to directly access the matched Contact-to-Product Module file.<\/p>\n Google Gemini\u2019s Contact-to-Product module file.\u00a0<\/em><\/p>\n This output file could then be imported into a CRM or marketing automation platform<\/a> (MAP) to drive follow-up messaging and campaign plans. It\u2019s one route to better, more personalized marketing based on a customer\u2019s existing product usage.<\/p>\n Clearly, this was a simplified example, but the underlying implications should not be underestimated. This natural language process eliminated not only the potential barriers of knowing how to structure more complex data, but also the time needed to complete this process manually as often as needed.\u00a0\u00a0<\/p>\n Even if it\u2019s not fully automated yet, this would open up further \u201cno-code\u201d integrations. Furthermore, what may have required more senior, technical resources can now rely on entry-level team member. With further attention and targeted training, this frees up both resources for more strategic campaign planning.<\/p>\n Does this mean these capabilities are limited to just marketers leveraging prompt engineering?\u00a0 No. Early indications are AI agents will be standardized across teams or productized as a new form of martech.<\/p>\n Following a similar approach that OpenAI took with custom GPTs, Google recently announced \u201cGems\u201d within Gemini<\/a> that I\u2019ll be testing further as an extension of this demo. For example, if the above process became a regular operational process, I could then publish a custom Gem across my organization\u2019s Google Workspace for my colleagues.<\/p>\n In a broader martech lens, these capabilities will be productized further as part of established no-code approaches like Zapier, or become the latest addition to an ever-expanding martech application landscape through AI agent marketplaces, such as Agent.ai<\/a>, recently launched by HubSpot<\/a> co-founder Dharmesh Shah.<\/p>\n With AI now trending toward agent-based capabilities, everyone will be a personal martech leader. While there will always be a spectrum \u2014 allowing for specialization and depth \u2014 basic training and\/or natural language prompting will enable previously \u201clow-adoption\u201d martech users to improve their campaigns.\u00a0<\/p>\n I\u2019m increasingly hopeful we can all start to reclaim our agency to improve our customer-focused marketing plans.<\/p>\n <\/p>\n \n<\/div>\n Contributing authors are invited to create content for MarTech and are chosen for their expertise and contribution to the martech community. Our contributors work under the oversight of the editorial staff<\/a> and contributions are checked for quality and relevance to our readers. The opinions they express are their own.<\/em><\/p>\n![\"Personal](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcagLtVSk-fNiQ53oERv4RvqfzlCidw6NdRcvxeJT_NykGsHRA5g0ALyG1QfhEtu9jnxertT6UodEbh4S3srSfOi1YMtWSR2cz8YrbneL2tXs8ALXld7QS1kESE9QRJCouGBu5yf_t-2v0QP5efrocI5IE?key=sKquEaQtPqdfTiUj25OT-g\")
Are agents just re-packaged versions of automation?<\/h2>\n
Yes<\/h3>\n
No<\/h3>\n
\n
The data \u2018look-up\u2019 use case<\/h2>\n
Sample file structure<\/h3>\n
![\"Sample](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcTLjvybaQs9UZ5G387LRBbDnQTJdJCuZa8PHp_dVNHj-kgPmYoylHavran-f9jVAV_cs-WrA9I7BQYslqm-yBJEsKVoFAQZVF6R-9RhAzMQAAhdT9Z23td_G7EYhm3UvvXB7tyS5aIhUmwzUgZ4lBiRbI?key=sKquEaQtPqdfTiUj25OT-g\")
![\"Sample](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXeNEQBYcvbWGr3UdNIkiLHRbcGlzeZbqict2OS7C8LL-bXgiZPUNfra4onSWzYaBaIPyl0WZJJ84qvk1dtNqaEQg0GtaAKG2Mpmav0uwFcRL7gd600oUnw7Z1q-D7u-CRosQ5Y2Bqm0wmaCj_jyqGfAsAs?key=sKquEaQtPqdfTiUj25OT-g\")
Now for the prompts<\/h3>\n
![\"Prompts](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcqaSLrBbM8OllXqKnswv3frwSnCBhuf4oOXDf7Fp5miIzilak802KVFXJqfNKy6_fi4LOeio7ynllcy0F2MlIrQFS0rB_c7RM1DE-0D0mLZjiDNC65FPrLUMageTJLM84L1D1wqGyEU9YRxhlmf5zLE1Sh?key=sKquEaQtPqdfTiUj25OT-g\")
![\"A](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXczPr8iV0tWHb6C9pUxQDiJ6xRbLSYegbr0EIhqxG8J_480qEfCCQcXTlkDNkldMDMJiQigfBr54iNEs3I9qundkRmLz7vX7pkW0pU6CCSWfqzZQ-ZO3jj_Ank9QvH7dyWbpJt7-0p3355Fuisd2y7bHb0?key=sKquEaQtPqdfTiUj25OT-g\")
![\"Google](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcOeQ5Z_qgr8ROLbMEfeewxjZ5oX9BFtC791p5Cg2rt_dLf6DxGvPVDGsk4XPHiuMMe7rBy8VK2MzumXeTBSvjQweY01araUnlUoWHf3lLNks35FxO-kgVUPB1IM4Vz8xiMnfjmYIOeLNsVuQPiTSg53Hg?key=sKquEaQtPqdfTiUj25OT-g\")
![\"Google](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXfRjLd27AkHayLwfyA32TxjHb3PhKSj_mrfDUZJwQWyEy8rC4C0-Y65RWG-ClbappWsLVcncatgwSl2lfDmYqg-nRJnVUKqFhPAdid1A4fQ5Mh4da1_WpXiCTQ4YyA3YBNFg0fnXkq9NBV1IpBLU65QfyTK?key=sKquEaQtPqdfTiUj25OT-g\")
![\"Google](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXf1KJMq3IlFdVc4hWmA8-94rlqFJAqM9UofKYJ6TdijUmAk_ZFWbaIL9l7iQLMFMKTty0VAsEwBpDcBajPOSJa05cWOlHsxDjb88XETo3N31sS5AveapjDypuEJi5pROkjkh5F8eHm5mNErpoHVhIKWd_Hv?key=sKquEaQtPqdfTiUj25OT-g\")
Improving customer campaigns<\/h2>\n
Implications for martech and marketers<\/h2>\n
![\"Personal](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcagLtVSk-fNiQ53oERv4RvqfzlCidw6NdRcvxeJT_NykGsHRA5g0ALyG1QfhEtu9jnxertT6UodEbh4S3srSfOi1YMtWSR2cz8YrbneL2tXs8ALXld7QS1kESE9QRJCouGBu5yf_t-2v0QP5efrocI5IE?key=sKquEaQtPqdfTiUj25OT-g\"\/)
![\"Sample](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcTLjvybaQs9UZ5G387LRBbDnQTJdJCuZa8PHp_dVNHj-kgPmYoylHavran-f9jVAV_cs-WrA9I7BQYslqm-yBJEsKVoFAQZVF6R-9RhAzMQAAhdT9Z23td_G7EYhm3UvvXB7tyS5aIhUmwzUgZ4lBiRbI?key=sKquEaQtPqdfTiUj25OT-g\"\/)
![\"Sample](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXeNEQBYcvbWGr3UdNIkiLHRbcGlzeZbqict2OS7C8LL-bXgiZPUNfra4onSWzYaBaIPyl0WZJJ84qvk1dtNqaEQg0GtaAKG2Mpmav0uwFcRL7gd600oUnw7Z1q-D7u-CRosQ5Y2Bqm0wmaCj_jyqGfAsAs?key=sKquEaQtPqdfTiUj25OT-g\"\/)
![\"Prompts](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcqaSLrBbM8OllXqKnswv3frwSnCBhuf4oOXDf7Fp5miIzilak802KVFXJqfNKy6_fi4LOeio7ynllcy0F2MlIrQFS0rB_c7RM1DE-0D0mLZjiDNC65FPrLUMageTJLM84L1D1wqGyEU9YRxhlmf5zLE1Sh?key=sKquEaQtPqdfTiUj25OT-g\"\/)
![\"A](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXczPr8iV0tWHb6C9pUxQDiJ6xRbLSYegbr0EIhqxG8J_480qEfCCQcXTlkDNkldMDMJiQigfBr54iNEs3I9qundkRmLz7vX7pkW0pU6CCSWfqzZQ-ZO3jj_Ank9QvH7dyWbpJt7-0p3355Fuisd2y7bHb0?key=sKquEaQtPqdfTiUj25OT-g\"\/)
![\"Google](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcOeQ5Z_qgr8ROLbMEfeewxjZ5oX9BFtC791p5Cg2rt_dLf6DxGvPVDGsk4XPHiuMMe7rBy8VK2MzumXeTBSvjQweY01araUnlUoWHf3lLNks35FxO-kgVUPB1IM4Vz8xiMnfjmYIOeLNsVuQPiTSg53Hg?key=sKquEaQtPqdfTiUj25OT-g\"\/)
![\"Google](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXfRjLd27AkHayLwfyA32TxjHb3PhKSj_mrfDUZJwQWyEy8rC4C0-Y65RWG-ClbappWsLVcncatgwSl2lfDmYqg-nRJnVUKqFhPAdid1A4fQ5Mh4da1_WpXiCTQ4YyA3YBNFg0fnXkq9NBV1IpBLU65QfyTK?key=sKquEaQtPqdfTiUj25OT-g\"\/)
![\"Google](\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXf1KJMq3IlFdVc4hWmA8-94rlqFJAqM9UofKYJ6TdijUmAk_ZFWbaIL9l7iQLMFMKTty0VAsEwBpDcBajPOSJa05cWOlHsxDjb88XETo3N31sS5AveapjDypuEJi5pROkjkh5F8eHm5mNErpoHVhIKWd_Hv?key=sKquEaQtPqdfTiUj25OT-g\"\/)